https://github.com/vmarkovtsev/bidisentiment

Two-way deep RNN for sentiment classification.
https://github.com/vmarkovtsev/bidisentiment

deep-neural-networks recurrent-neural-networks sentiment-analysis

Last synced: 22 days ago
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Two-way deep RNN for sentiment classification.

• Host: GitHub
• URL: https://github.com/vmarkovtsev/bidisentiment
• Owner: vmarkovtsev
• License: mit
• Created: 2018-03-06T14:57:00.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-03-16T18:35:01.000Z (over 6 years ago)
• Last Synced: 2024-06-20T22:50:00.351Z (5 months ago)
• Topics: deep-neural-networks, recurrent-neural-networks, sentiment-analysis
• Language: Python
• Size: 40.3 MB
• Stars: 62
• Watchers: 3
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        
BiDiSentiment [![GoDoc](https://godoc.org/gopkg.in/vmarkovtsev/BiDiSentiment.v1?status.svg)](http://godoc.org/gopkg.in/vmarkovtsev/BiDiSentiment.v1) [![Build Status](https://travis-ci.org/vmarkovtsev/BiDiSentiment.svg?branch=master)](https://travis-ci.org/vmarkovtsev/BiDiSentiment)

=============

Text sentiment analyser for Go and Python.

Runs on top of Tensorflow. The architecture is a bidirectional two-level character LSTM.

![Happy and sad gophers](doc/logo_small.png)

Installation of the inference module

------------------------------------

```

go get gopkg.in/vmarkovtsev/BiDiSentiment.v1/...

```

Usage of the inference module

-----------------------------

For every line in stdin, the command line tool prints the probability of the negative sentiment

(the probability of the positive sentiment is 1 - negative).

```

echo "This is the worst movie I have ever seen, it sucks balls!" | sentiment 

0.9961139

echo "This is the best movie I have ever seen, just love it!" | sentiment

0.000769752

```

API expects string batches for performance reasons:

```go

import "gopkg.in/vmarkovtsev/BiDiSentiment.v1"

func main() {

  session, _ := sentiment.OpenSession()

  defer session.Close()

  result, _ := sentiment.Evaluate(

    []string{"This is the best movie I have ever seen, simply love it!"},

    session)

  println(result[0])

}

```

Science

-------

![arch](doc/arch.png)

We scan through the text by byte in both directions, and the length is constrained to 180 bytes.

The training is written in Python and is based on Keras and Tensorflow. The achieved accuracy

on 20% validation is **87%**. The train dataset was

[1.5 million tweets](http://thinknook.com/twitter-sentiment-analysis-training-corpus-dataset-2012-09-22/).

The default parameters were used. The model easily overfits, so only the first 5 epochs were used.

![validation](doc/valid.png)

Update the model in the Go inference application by executing

```

go-bindata  -nomemcopy -nometadata -pkg assets -o assets/bindata.go  model.pb english.json

```

Contributins

------------

...are welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) and [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md).

License

-------

MIT, see [LICENSE](LICENSE).